ARTÍCULOS ESPECIALES

CONSENSOS

 

RÉPLICA

Assume leadership in the delivery of top quality home healthcare^*

Asumir el liderazgo en la prestación de asistencia domiciliaria de calidad

 

 

Susan Procter¹

¹Registered Nurse. Graduate in Social Science and Administration. PhD in Nursing. Professor of Primary Health Care Research. City Community and Health Sciences. City University, London, UK

*Contenidos de la Mesa Redonda desarrollada dentro del Simposio Internacional sobre Investigación en Enfermería Comunitaria, celebrado en Granada, en la Escuela Andaluza de Salud Pública, el 4 y 5 de octubre de 2007

Dirección para correspondencia

 

 

Introduction

Firstly I would like to thank Joan Segura for presenting such a comprehensive review of the European situation in relation to dependent individuals. I think we have much to learn from each other and his paper identifies important areas for comparative research. In my response I would like to focus on the public health issues arising from caring for dependent individuals and to locate the role of the nurse in the context of public health policy and practice.

Joan begins by discussing the "morbidity compression theory" which points to a modest drop in the prevalence of dependency as the population ages. Clearly a key objective of any health care system would be to maximise the number of healthy life years experienced by people as they age and reduce the number of years spent living with dependency.

This policy objective was recently reviewed in the UK by Derek Wanless who produced the Wanless Report (UK Department of Health 2002). Prior to the 2002 Treasury spending review Derek Wanless was asked to assess "the financial and other resources required to ensure that the NHS can provide a publicly funded, comprehensive, high quality service on the basis of clinical need and not ability to pay". The Review looked at health outcomes and costs arising from three different scenarios over the next 20 years:

1 Solid progress - People become more engaged in relation to their health: life, expectancy rises considerably, health status improves and people have confidence in the primary care system and use it more appropriately. The health service is responsive with high rates of technology uptake and a more efficient use of resources;

2 Slow uptake - There is no change in the level of public engagement: life, expectancy rises by the lowest amount in all three scenarios and the health status of the population is constant or deteriorates. The health service is relatively unresponsive with low rates of technology uptake and low productivity;

3 Fully engaged - Levels of public engagement in relation to their health are high, life expectancy increases go beyond current forecasts, health status improves dramatically and people are confident in the health system and demand high quality care. The health service is responsive with high rates of technology uptake, particularly in relation to disease prevention. Use of resources is more efficient. I don't think you can see it but in this slide the elderly women exercising in the gym is receiving oxygen via a nasal catheter from a oxygen cylinder at her side. The other patients are all using a variety of portable medical devises while maintaining full engagement in everyday life.

Arguably in the UK and possibly in most of Europe we are in the slow uptake scenario where health care is dominated by the hospital sector and by institutional care. In his paper Joan describes how the allocation of resources to support the elderly population is moving away from institutional care and towards supporting patients in the community. However, as he points our while there is a general commitment at a policy level in relation to supporting home care delivery there are huge differences in how it is organised both in terms of service delivery, information systems and resource models.

The Wanless Report recommended that in the UK we move towards the "fully engaged" scenario. The fully engaged scenario was the least expensive scenario modelled and delivered better health outcomes. In absolute expenditure terms the gap between the best and worst scenarios is large - around £30 billion by 2022/23, or half of current NHS expenditure.

However as Derek Wanless recognised moving towards the fully engaged scenario presents considerable challenges for the NHS and for other Western health care systems which I want to explore in this response.

As Joan rightly points out European health care systems are facing a considerable challenge with the rising elderly population which can only be offset by greater population engagement in health The next two slides demonstrate changes in life expectancy for women and then men respectively in three European countries from 1841 to 2001 at age 50. They indicate that although life expectancy at age 50 has been rising gradually throughout this period, there has been a significant increase in female life expectancy at age 50 from about 1950 onwards and in male life expectancy at age 50 from about 1970 onwards.

These relatively recent rises in life expectancy at age 50 have been bought about by a combination of improved living standards and improvements in medical technology and service delivery.

The next slide shows life years lost in women in 10 European Countries. Unfortunately Spain wasn't included in this analysis. Life years lost is used to measure the total number of years lost due to premature deaths. The usual benchmark for premature deaths is 75 years. For instance if a person died at the age of 60, that person lost 15 years due to dying prematurely. Life years lost are then aggregated for all people dying below the age of 75 and in this slide presented by broad categories of causes of death. When life years lost are compared across countries as they are here, they have to be standardised otherwise a country with larger population will almost always lose more life years than a country with a smaller population.

The vertical axis measures number of life years lost and is graded between 50,000 life years lost and 500,000. The main causes of premature death are cancers, light purple, other causes dark purple, diseases of the circulatory system, light yellow and external causes (accidents suicides light blue). Infectious diseases are quite small (dark blue line at the bottom, difficult to see).

This kind of data informs public policy and gives rise to attempts to optimise health and social care policies in order to increase the number of healthy years a person lives and decrease the number of years lost or spent debilitated by chronic illness. It underpins more population based approaches to health care provision both globally and nationally

In his paper Joan goes onto to discuss the increase in people presenting with chronic diseases with forecasts pointing to a significant rise in the number of unscheduled, unrelated hospital admissions. Joan highlights the prevalence of co-morbidity as an associated factor in this. Most of the research and guidelines on chronic disease have focused on singular diseases, e.g. hypertension, diabetes, asthma. However, there is increasing awareness that the patient's experience of chronic disease is cumulative. Estimates vary but there is evidence that up to two-thirds of patients with one of the five most common chronic diseases also have two or more chronic conditions and typically patients in the top 10% of service users have 4 or more chronic conditions (Department of Health 2004).

This relates back to debates about the relative importance of public health approaches to prevention when compared with the impact of health service interventions. Bartley et al. (1997) highlight how as individuals progress through life there are critical periods when they are at risk of acquiring attributes or experiences that predispose towards illness. These include intergenerational factors such as maternal nutrition during pregnancy, unemployment, job insecurity, the onset of chronic illness and exit from the labour market. Bartley et al. (1997) conclude that without support from appropriate health and social policies these events can have effects on future patterns of individual and community health. There, are therefore continuing debates, as Joan highlights in his paper, in relation to where to locate public expenditure on health and social care in order to maximise health outcomes. This was recognised by Archie Cochrane and his colleagues, the founding fathers of the RCT, when they commented that 'health service factors are relatively unimportant in explaining the differences in mortality rates between developed countries (Cochrane et al. 1978 p.205).

 

Do health services make a difference to individual and population health outcomes?

According to Lakhani et al. (2005) attempts to assess the contribution of health services to the entire population (not just those using health services) have relied on population based indicators of potentially avoidable mortality such as those illustrated earlier. Causes of death are included if there is evidence that they are amenable to healthcare interventions and-given timely, appropriate, and high quality care-death rates should be low among the age groups specified. Healthcare intervention includes preventing disease onset as well as treating existing disease.

Lakhani et al. (2005) describe variations in deaths that were amenable to healthcare interventions and those that were not in people aged less than 75 years during 1998-2002. They indicate that in the UK mortality from amenable causes (including ischaemic heart disease) fell from 164 to 132 per 100.000 -an average annual improvement of 5.7%. Mortality from ischaemic heart disease improved by an average of 6.5% a year, and mortality from other amenable causes improved by 5.0% a year. This compares with an annual improvement of only 1.0% for mortality from causes not considered amenable. The findings of Lakhani et al. (2005) reinforce the findings of Bartley et al. (1997) as both point to the observation that today's survival and death rates are at least partly a reflection of the quality of earlier health care. This applies particularly to primary and secondary prevention of conditions such as heart disease, stroke, diabetes, some cancers, and diseases of childhood. The converse of this is that many of the benefits to health from improved care today will not be seen for many years. Lakhani et al. (2005) conclude that "one of the implications of this is that a comprehensive assessment of the quality of a healthcare system should include formal forecasts of the longer term effects of recent changes in provision and activity".

 

The role of data in improving health outcomes

There are many recognised difficulties with using official statistics to measure health outcomes (Leatherman and Sutherland 2003). In particular the fact that global targets such as reduced death rates from heart disease and stroke, cancer, suicide, and accidents are all outcome indicators but, are only partly attributable to any one service intervention and are in fact a product of the whole system. This makes it difficult for single services such as health services or social services to measure their direct effect on any changes in these indicators. Never-the-less some practitioners in primary care are pioneering innovative ways of working with these indicators. For instance McColl et al. (1998) have developed an evidenced based approach to identify important primary care interventions of proved efficacy and suggest performance indicators that could monitor their use. An example of these taken from their article, in relation to hypertension is given in slide 4.

McColl et al. (1998) suggest that their approach to indicators is more likely to help turn evidence into everyday practice and to have an impact on the population's health, than simple reporting of trend data.

The above discussion highlights some of the data and information sources being used by Governments in Europe to measure effectiveness of service provision. Within Europe some countries such as Finland have adopted a public health approach to information management and outcome measurements.

The Finnish Health 2015 programme is a cooperation programme that presents 8 targets for public health. The concepts 'settings of everyday life' and 'course of life' play a key role in this programme. The public health programme in Finland is supported by the SotkaNet Indicator Bank which contains information on welfare and health among the Finnish population. The Indicator Bank is maintained by STAKES, and the aim is to produce a time series of municipality-based welfare and health data in Finland from 1990 onwards by gathering data on a yearly basis. Indicator data can be searched according to different geographical areas, and the results are presented in absolute numbers or in percentages. In addition, indicator descriptions provide information on data content, years covered, possible restrictions, as well as advice on data interpretation. A description of the way these data can be used to inform local integrated planning projects which work across the whole local system of provision has been given by Luukkainen (2006).

The Finnish approach can be contrasted with the approach taken in the US and being considered in the UK. In the US trends in hospital and service utilisation recorded through insurance based health care systems are used to monitor service use and to identify patient populations who might benefit from timely earlier intervention (Lewis and Dixon 2004). The transfer of this system to the UK has influenced the development of a commissioning model for health care provision. However, in the UK apart from GP data all patient data is episodic ie 5 admissions to hospital could be 5 different people or the same person 5 times. At the moment our central information systems can not distinguish between these two scenarios. Local systems are being set up by community matrons and local health service managers to identify high hospital users. Until recently many of these have relied on hand searches of manual data sources as centralised systems could not be programmed to produce this information. The next slide gives an example taken from a local project in NE London.

 

Slide 5 provides information on the composition of five District Nurse caseloads in one locality in NE London on 17^th April 2006, each District Nurse was GP attached. These figures are self-report as no other information was available in the in the local health care system on District Nurse caseload reflecting current difficulties with NHS information systems which are familiar across the UK. The data from District Nurses indicates considerable disparities in the District Nurse caseload even within a given locality. Of the 709 patients on the caseload only 39 patients are classified by District Nurses as short term patients, the rest were classified as having one or more Long Term Conditions. Short term patients are usually discharged after one month and include people with post-operative wound dressings and needing application of eye drops following surgery. Most long term condition patients on the District Nurse caseload have multiple conditions with the exception of patients with incontinence. Based on District Nurse subjective calculations 124 of the 709 long term condition patients were classified as highly complex. There was a large variation in the number of highly complex cases on each individual District Nurse caseload. Additionally the District Nurses were looking after 40 palliative care patients.

The District Nurse data reflects an important area of care about which we have very little information. Although considerable progress is being made across the UK to understand the needs of these patients and to develop competencies at a local level, it is difficult to identify the skill mix and workforce required to provide for these needs in a systematic and planned way if such information is absent from formal information systems used to inform policy and plan services. The District Nurse caseload is an example of the actual data required in the UKto identify how workforce skills are located within a framework of health care needs that are not currently understood or evidenced (Kendrick and Conway 2003).

More recently The PARR formula (Billings et al. 2006) has been made available to nurses in the UK to identify patients at risk of hospital readmission. The PARR (Patients at Risk of Readmission) relies on data already in the system on patients. It collates data across a range of pre-existing electronic data sources and undertakes regression analysis to assess each individuals' risk of readmission. Its results are given on a sliding scale. In other words it can only identify a small percentage of the total number of patients at risk of readmission with high level accuracy. It can identify a much larger percentage of patients at risk of hospital readmission but with much lower accuracy ie a proportion of the patients identified will turn out not to be at risk of hospital readmission.

PARR is only able to use information currently available on existing electronic information systems. It cannot utilise the predictive potential of important drivers of hospital utilisation such as anxiety, social isolation, carer support and individual patient response to illness (Procter et al. 2001) found to heavily influence hospital admission or use the important information collected by District Nursing Services if such data are not routinely available electronically.

In a national workshop organised as part of a study I am currently undertaking with Professor Sally Kendall and colleagues from the University of Hertfordshire nurses involved in using the PARR formula described how they still felt the need to supplement it with more individual information about patients usually derived manually rather than electronically in order to target patients for Community Matron intervention.

In the UK a number of local health authorities are addressing this problem by setting up Electronic Medical Information System (EMIS) using NHS data available electronically and linking it to Electronic Single Assessment Process (ESAP) data produced by Social Services at a local level. Data warehousing is an integrative technique that is being used to identify which services patients are using and where gaps in provision exist. However, these are still being developed and are experiencing numerous technical and data protection obstacles. Consequently currently in the UK it is not possible to identify the total population living with a long term condition in any given locality. Information available in GP surgeries does not collate multi-pathology and therefore does not give any indicator of total numbers of patients with a long term condition, disease severity or level of care need.

 

Information Systems, Evidence and Models of Service Delivery

In his paper Joan describes a range of new service delivery models, what I hope my paper demonstrates is that the link between evidence, information systems and models of service delivery influences the extent to which these models can be transferred from one context to another and in particular from one national context to another national context where information systems may not support key features of the model being transferred.

This has been demonstrated recently in the UK in the evaluation of Evercare (Gravelle et al. 2006). Here the transfer of the US model of care to the UK was hampered by the very different information systems in the NHS which made it difficult to identify similar patient populations to whom the Evercare model could be generalised. Moreover, the model was only partially replicated as the home care services which are a key feature of Evercare in the US, were not commissioned in the UK pilots as they do not form part of the funding remit of the local health care providers. Consequently the benefits of Evercare found in the US did not transfer effectively to the UK setting.

So while it is clear that the need for integrated health and social care services has been recognised at an international level in most western health services. Models of delivery have to be developed in relation to local information contexts and funding streams. Understanding local health and social care information systems and funding streams is therefore a crucial element in developing effective local integrated service models. Understanding how these models arose from local information sources and funding streams will help us to anticipate whether or not these models will transfer successfully into different settings with different information sources.

In relation to this Bodenheimer et al. (2002) have identified 6 interlocking and essential elements which are recognised internationally as essential components of an effective system of long term conditions management. Strategy (Robinson et al. 2002, UK Department of Health 2005, Glasgow et al. 2001, National Health Service Partnerships 2001).

1. Community resources and policies - use of local voluntary sector, community resources, self-help groups, day centres etc.

2. Health care organisation - this recognises that innovations in the delivery of chronic care will only be sustained if health care organisations prioritise chronic care quality provision through priority goal setting, rewards and reimbursements.

3. Self-management support - This recognises that with chronic illness, patients themselves become the principal care givers and ways of supporting them to sustain this role are central to the model.

4. Delivery system design - Acute care must be separated from the planned management of chronic conditions and a clear division of labour agreed.

5. Decision support - Most chronic diseases have established evidence based guidelines and protocols and these should be integrated into daily practice through reminders.

6. Clinical information - should consist of three parts I). As reminder systems that help primary care teams comply with clinical and practice guidelines II). As feedback to health professionals providing information on chronic illness measures such as hypertension or lipid levels III). As registries for planning individual patient care and conducting population based care. (Bodenheimer et al. 2002).

The role of information systems is clearly crucial if we are to move towards implementing the type of model described by Bodenheimer and colleagues. We cannot prioritise chronic care if we cannot identify it at an individual level in our information systems.

Changing the relationship that patients have with illness and the health service is also important. Health care systems dominated by acute hospital provision tend to create dependency on technology and instil a false sense of security in patients that the hospital will provide the cure and they don't have to make any significant changes to their life style. This approach is prevalent in the UK today and contrary to the 'fully engaged' scenario described by Wanless discussed earlier.

It is perhaps for this reason that Bodenheimer and colleagues are advocating the separation of acute care from the planned management of chronic conditions. However, acute care is a very powerful stakeholder in most western health care systems and is unlikely to easily accept a more diminished role.

A key problem that still needs to be addressed is the failure to identify what is meant by a long term condition, exactly which conditions/diseases does it include? Very little guidance is available nationally or internationally on this. The difficulties in identifying or classifying long term conditions reflects an historical emphasis on specialist disease management models based on acute episodes of care. These specialist management models have developed in relative isolation from each other, focused on specific treatment modalities, rather than the more generic prevention strategies now being advocated which focus on diet, exercise, smoking cessation etc. (Wanless 2002, Department of Health 2005).

At the heart of this debate is a recognition that in relation to long term conditions and increasing dependency in the elderly population further specialisation is likely to just add to existing fragmentation (Grumbach 2003). Current attempts at reform in the UK have utilised case management as a system for integrating care for individual patients at a local level. However considerable debates continue as to the skills sets and competencies required by case managers. Arguably elements of case management can be found in the descriptions of most health and social care professional practice from Social Workers to GPs (Robins and Birmingham 2005, Starfield et al. 2003). Given that most patients making high use of health and social services will be suffering from one or more long term conditions, case management cannot be confined to a few dedicated professionals such as community matrons. In the UK Community Matrons are distinguished by having both case management and high level physical assessment skills which allow them to diagnose fluctuations in severity of disease process and alter medication management accordingly. The difficulty that we still face is identifying those patients who would benefit most from access to the specific skills sets of community matrons. We also need to know at what stage in the progression of chronic diseases and dependency the skills of community matrons can be most effectively utilised to reduce the onset of dependency among this population and the number of life years lost by their local population.

 

Conclusions

In his conclusions Joan identified the opportunities the current situation creates for community nurses to assume leadership in the delivery of top quality home healthcare. I totally agree with this conclusion and suggest that if community nurses focus on case management and physical assessment skills they will be well placed to deliver high quality home healthcare. To assume leadership however, I think will require community nurses to understand and use the types of data described in this paper to identify where to target their expertise and to demonstrate their impact on population health. It is important that future health information systems include core nursing data.

As demonstrated in this paper many population based health outcome indicators cannot be linked to the specific contribution of any single organisation or professional group. The future lies in intersectorial working practices in which the contribution each of the parts (organisation, service or professional group) makes to the whole (population health outcomes) can be readily identified. Community nurses are well placed to take the lead in establishing such systems.

 

References

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Dirección para correspondencia:
s.procter@city.ac.uk

Manuscrito recibido el 23.12.2007
Manuscrito aceptado el 27.8.2008